Comparison
deeplake vs Awesome-LLMOps
Verdict
Pick deeplake if deeplake is an AI Data Runtime for Agents designed with serverless Postgres and multimodal data lake support, targeting scalable retrieval and training capabilities; pick Awesome-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Markdown twin · deeplake alternatives · Awesome-LLMOps alternatives
GraphCanon updated today
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Trust & integrity
| Signal | deeplake | Awesome-LLMOps |
|---|---|---|
| Maintenance | Steady (87d since push) As of 3d · github_public_v1 | Slowing (91d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3d · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- deeplake
- AI Data Runtime for Agents with scalable retrieval and training features
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- deeplake
- 9.2k
- Awesome-LLMOps
- 5.9k
Forks
- deeplake
- 721
- Awesome-LLMOps
- 993
Open issues
- deeplake
- 63
- Awesome-LLMOps
- 247
Language
- deeplake
- C++
- Awesome-LLMOps
- Shell
Adopt for
- deeplake
- Deeplake is an AI Data Runtime for Agents designed with serverless Postgres and multimodal data lake support, targeting scalable retrieval and training capabilities.
- Awesome-LLMOps
- Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Persona
- deeplake
- -
- Awesome-LLMOps
- -
Runtime
- deeplake
- -
- Awesome-LLMOps
- -
License
- deeplake
- Deeplake uses the Apache-2.0 license, allowing free use in both open source and commercial projects with attribution.
- Awesome-LLMOps
- CC0-1.0
Last pushed
- deeplake
- May 21, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- deeplake
- Data & Retrieval, Model Training, Vector Databases
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- deeplake
- Steady (60%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- deeplake
- 87d
- Awesome-LLMOps
- 91d
Open issues (now)
- deeplake
- 63
- Awesome-LLMOps
- 247
Stars delta
- deeplake
- +16 (30d)
- Awesome-LLMOps
- +28 (30d)
Open issues delta
- deeplake
- -6 (30d)
- Awesome-LLMOps
- +66 (30d)
Full report
- deeplake
- Trust report
- Awesome-LLMOps
- Trust report
Choose deeplake if…
- deeplake is primarily C++; Awesome-LLMOps is Shell.
- License: deeplake is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- Pricing: Pricing details are not specified for Deeplake's public repository..
- Requirements: Deeplake can be installed using pip, making it accessible via the command `pip install deeplake`..
- Tags unique to deeplake: agent, agentic-rag, ai, computer-vision.
- Also covers Vector Databases.
- When you are developing applications that require seamless integration with AI agents, as Deeplake supports agent-centric design.
When NOT to use deeplake
- If your project does not benefit from an agent-centric architecture and you primarily require traditional database operations without multimodal features.
- When cost control is critical and serverless PostgreSQL might introduce variable costs compared to on-premises solutions for data retrieval and training.
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; deeplake is C++.
- License: Awesome-LLMOps is CC0-1.0, deeplake is Apache-2.0.
- Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, Evaluation & Observability, Inference & Serving, LLM Frameworks, Speech & Audio.
- - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
When NOT to use Awesome-LLMOps
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list.
- - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
Explore
deeplake trust report →Awesome-LLMOps trust report →Data & Retrieval category →Model Training category →Vector Databases category →Computer Vision category →Evaluation & Observability category →Inference & Serving category →LLM Frameworks category →Speech & Audio category →All comparisonsStack workflowsTrending tools
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (activeloopai/deeplake) · observed Aug 17, 2026
- GitHub forks (activeloopai/deeplake) · observed Aug 17, 2026
- Last push (activeloopai/deeplake) · observed May 21, 2026
- License file (Apache-2.0) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 9, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Aug 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: deeplake 9.2k · Awesome-LLMOps 5.9k (synced Aug 17, 2026).
Common questions
- What is the difference between deeplake and Awesome-LLMOps?
- deeplake: AI Data Runtime for Agents with scalable retrieval and training features. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories.
- When should I choose deeplake over Awesome-LLMOps?
- Choose deeplake over Awesome-LLMOps when deeplake is primarily C++; Awesome-LLMOps is Shell; License: deeplake is Apache-2.0, Awesome-LLMOps is CC0-1.0; Pricing: Pricing details are not specified for Deeplake's public repository.; Requirements: Deeplake can be installed using pip, making it accessible via the command
pip install deeplake.; Tags unique to deeplake: agent, agentic-rag, ai, computer-vision; Also covers Vector Databases; When you are developing applications that require seamless integration with AI agents, as Deeplake supports agent-centric design. - When should I choose Awesome-LLMOps over deeplake?
- Choose Awesome-LLMOps over deeplake when Awesome-LLMOps is primarily Shell; deeplake is C++; License: Awesome-LLMOps is CC0-1.0, deeplake is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Evaluation & Observability, Inference & Serving, LLM Frameworks, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- When should I avoid deeplake?
- If your project does not benefit from an agent-centric architecture and you primarily require traditional database operations without multimodal features. When cost control is critical and serverless PostgreSQL might introduce variable costs compared to on-premises solutions for data retrieval and training.
- When should I avoid Awesome-LLMOps?
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
- Is deeplake or Awesome-LLMOps more popular on GitHub?
- deeplake has more GitHub stars (9,224 vs 5,915). Stars measure visibility, not whether either tool fits your constraints.
- Are deeplake and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (deeplake: Apache-2.0, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to deeplake or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at deeplake alternatives and Awesome-LLMOps alternatives (deeplake markdown twin, Awesome-LLMOps markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, deeplake or Awesome-LLMOps?
- deeplake: Steady. Awesome-LLMOps: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for deeplake and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: deeplake trust report; Awesome-LLMOps trust report.